| Control (N=20) |
AD (N=24) |
Overall (N=44) |
|
|---|---|---|---|
| age | |||
| Mean (SD) | 79.9 (7.70) | 77.5 (8.24) | 78.6 (7.99) |
| Median [Min, Max] | 79.5 [65.0, 92.0] | 77.5 [64.0, 92.0] | 78.5 [64.0, 92.0] |
| sex | |||
| F | 11 (55.0%) | 12 (50.0%) | 23 (52.3%) |
| M | 9 (45.0%) | 12 (50.0%) | 21 (47.7%) |
| diagnosis | |||
| Control | 20 (100%) | 0 (0%) | 20 (45.5%) |
| AD | 0 (0%) | 24 (100%) | 24 (54.5%) |
| Braak | |||
| 0 | 5 (25.0%) | 0 (0%) | 5 (11.4%) |
| 1 | 3 (15.0%) | 0 (0%) | 3 (6.8%) |
| 2 | 12 (60.0%) | 0 (0%) | 12 (27.3%) |
| 3 | 0 (0%) | 3 (12.5%) | 3 (6.8%) |
| 4 | 0 (0%) | 1 (4.2%) | 1 (2.3%) |
| 5 | 0 (0%) | 8 (33.3%) | 8 (18.2%) |
| 6 | 0 (0%) | 12 (50.0%) | 12 (27.3%) |
| amyloid | |||
| Mean (SD) | 0.422 (0.333) | 3.61 (2.68) | 2.70 (2.69) |
| Median [Min, Max] | 0.334 [0.0456, 0.968] | 3.13 [0.0577, 8.38] | 1.48 [0.0456, 8.38] |
| Missing | 12 (60.0%) | 4 (16.7%) | 16 (36.4%) |
Minimum count: 10
Minimum percentage of sample with transcript: 0.5
Filter for protein coding genes only: TRUE
Transformation method: rlog
Apply batch correction: TRUE Batch variable
name: No batch
Remove sample outliers: TRUE
Dependent variable: diagnosis
Dependent variables levels: Control, AD
Covariates: age, sex, PMD
Keep only protein coding genes in the analysis:
TRUE
Number of non protein coding genes identified:
3.9155^{4}
Percentage of genes filtered out: 0.3350486
Number of protein coding genes kept for analysis:
1.9729^{4}
Minimum count: 10
Minimum percentage of sample with transcript: 0.5
Filter for protein coding genes only: TRUE
Number of genes filtered: 4884
Percentage of genes filtered out: 0.2475544
Number of genes kept for analysis: 1.4845^{4}
Transformation method: rlog
Apply batch correction: TRUE
Batch variable name: No batch
Remove sample outliers: TRUE
Number of sample outliers: 0
Percentage of sample outliers: 0
No sample outlier detected!
After filtering, 44039 genes were removed from the analysis. The final matrix for analysis consisted of 14845 proteins and 29 samples.
## [1] 29 24## [1] 29 29
## Using protein as id variables
## Using protein as id variablesApply protein filtering: TRUE
Minimum percentage of sample with non missing protein
abundance: 0.5
Imputation method: minimum_value Apply batch
correction: TRUE
Batch correction method: median_centering
Batch variable name: No batch
Remove protein outliers: TRUE
Remove sample outliers: TRUE
Denoising: TRUE
Dependent variable: diagnosis
Dependent variables levels: Control, AD
Covariates for denoising: PMD, sex, age
Apply protein filtering: TRUE
Minimum percentage of sample with non missing protein
abundance: 0.5
The parameters chosen for filtering of proteins ensured that at least 50 % of samples had abundance data for a single protein.
Number of protein filtered: 322
Percentage of protein filtered out: 0.0997522
Number of protein kept for analysis: 2906
Imputation method: minimum_value
Batch correction method: median_centering
Batch variable name: No batch
Remove protein outliers: TRUE
Remove sample outliers: TRUE
Number of protein outliers: 266
Percentage of feature outliers: 0.0915348
Number of sample outliers: 0
Percentage of sample outliers: 0
No sample outlier detected!
After filtering, 588 proteins were removed from the analysis. The final matrix for analysis consisted of 2640 proteins and 39 samples.
## Using id, batch as id variables## Using id, batch as id variables
## Using protein as id variables
## Using protein as id variablesExperimentList class object of length 4: * [1] rna_raw: SummarizedExperiment with 58884 rows and 29 columns * [2] protein_raw: SummarizedExperiment with 3228 rows and 39 columns * [3] protein_processed: data.frame with 2640 rows and 39 columns * [4] rna_processed: data.frame with 14845 rows and 29 columns
The maximum correlation between mRNA expression and protein abundance is achieved for the RPH3A gene with 0.8859244 correlation.
Omix v1.0.0 – 2023-06-15 14:32:21
A report by Omix